A new internet game called Momo is challenging users to commit suicide. The game originated on Facebook and is now circulating on WhatsApp. Microsoft is clamping down on the sick "Momo suicide challenge," which recently infiltrated the wildly popular online game "Minecraft." The tech giant owns "Minecraft" developer Mojang. The vile "Momo suicide game" has been garnering attention after spreading on WhatsApp, prompting police warnings.

There is no doubt that technology is changing where we work, how we work and when we work. With that comes many advantages – increased productivity, efficiency and potential to increase the bottom line. It can also facilitate increased agile working for a workforce striving to improve work/life balance. But technology also brings challenges, with which UK employers are continuing to wrestle as they seek to attract, engage and retain a future-ready workforce. The impact of technology was highlighted as a key issue in the Future Chemistry report we recently produced in partnership with clients from across the UK.

The technique was developed by an international team led by a group from the Max Planck Institute for Informatics and including researchers from the University of Bath, Technicolor, TU Munich and Stanford University. The work, called Deep Video Portraits, was presented for the first time at the SIGGRAPH 2018 conference in Vancouver on 16th August. Unlike previous methods that are focused on movements of the face interior only, Deep Video Portraits can also animate the whole face including eyes, eyebrows, and head position in videos, using controls known from computer graphics face animation. It can even synthesise a plausible static video background if the head is moved around. Hyeongwoo Kim from the Max Planck Institute for Informatics explains: "It works by using model-based 3D face performance capture to record the detailed movements of the eyebrows, mouth, nose, and head position of the dubbing actor in a video. The research is currently at the proof-of-concept stage and is yet to work at real time, however the researchers anticipate the approach could make a real difference to the visual entertainment industry. Professor Christian Theobalt, from the Max Planck Institute for Informatics, said: "Despite extensive post-production manipulation, dubbing films into foreign languages always presents a mismatch between the actor on screen and the dubbed voice.

These artificial cities and towns were built for all kinds of reasons. One example in Russia was dressed up with faux facades for a visit from Putin in 2013, closely following the mold of the Potemkin villages of yore. Some, like China's replica cities, were built by developers to architecturally mimic Western cities like Paris. Others were constructed by the military or self-driving car companies to provide realistic urban environments in which to practice maneuvers or test new tech. "These Potemkin villages are for me an interesting symbol of the sometimes absurd developments of our society," Sailer says, referring to the illusions, fakes, and manipulations that governments and companies employ to maintain and extend their power.

Algorithms and supercomputers are revolutionizing the insurance industry. Germany has no intention of being left behind. The Bavarian dialect is difficult even for native German speakers in other parts of the country to understand, but IBM's Watson artificial intelligence application easily deciphers calls from customers of Bavarian insurer VKB. More responsive customer service is just one way AI is transforming staid German insurers as they undergo a digital revolution that is changing everything from how quickly claims can be paid to figuring out who is trying to defraud the company. "Insurers, banks, financial services as we know them today won't exist in 10 to 15 years," Christian Rieck, professor at Frankfurt University of Applied Sciences, wrote in a recent book about robots in finance.

Trust is frustratingly scattershot, seemingly conflating two separate issues: that of AI that surpasses us and has about as much concern for us as we have for the ants we thoughtlessly trod on; and the unimaginably vast amounts of data about everything we collect everyday, the abuse of which, such as by Cambridge Analytica, can literally change the course of entire nations (see: the electoral triumphs of Trump and Brexit). There are connections -- AI learns by ingesting raw data, and will learn how to manipulate us even better than humans holding that data can -- but that never quite gels here. Perhaps the film's very brief running time -- under 80 minutes -- wasn't the best choice: these are matters that could keep a documentary TV series busy for many weeks. In the reminder that many scientific advances -- such as, say, nuclear fission -- once deemed to be impossible or off in the distant future have come to pass very soon after such predictions are made. In the warning that we very quickly get used to and utterly blasé about technology that initially seems horrifying.

AUNTIE BEEB is embracing the AI revolution with two nights of programming generated by a neural network offering a juxtaposition between bleeding edge tech and vintage television. Eagle-eyed viewers will have spotted'BBC 4.1 - Artificial Intelligence TV' has been trailing for a couple of weeks, assuring viewers they can'Relax - It's going to be fine'. Alongside programming about AI itself, 'Made by Machine: When AI met The Archive' will show a range of classic clips from over 250,000 shows since 1953, selected by an AI, trained to know what BBC Four is, what it shows and what its viewers will like. The experimental programming has unearthed some'hidden gems' that haven't been seen in years, and which manual research alone would have taken hundreds of hours of research - if indeed they were found at all. The slight elephant in the room is that, given that the BBC recycled and junked many master tapes during the 1970s and 1980s, some of the suggestions may no longer exist.

On the dot, a London, UK-based machine learning-driven last mile delivery startup, received an additional investment of £8m from CitySprint Group, its parent company, which is itself PE-funded. The company, which has raised £17.7m in total funding since its establishment in 2015, intends to use the funds for product development, senior hires and international expansion. Led by Santosh Sahu, CEO, and recently appointed Chief Revenue Officer Ben Nowlan, and Technical Director Taher Khaliq, On the dot – which now combines On the dot and LastMileLink Technologies – provides national and local retailers with an online portal or API, to intelligently display dynamic time windows, allowing their customers to select deliveries at the time most convenient to them. The company's fastest delivery to date is 275 seconds. On the dot's roster counts more than 100 customers including ASOS, Currys PC World, Dixons and Wickes.

Researchers from the Institute of Cyber Intelligence Systems at the National Research Nuclear University MEPhI (Russia) have recently developed a new learning model for the restricted Boltzmann machine (a neural network), which optimizes the processes of semantic encoding, visualization and data recognition. The results of this research are published in the journal Optical Memory and Neural Networks. Today, deep neural networks with different architectures, such as convolutional, recurrent and autoencoder networks, are becoming an increasingly popular area of research. A number of high-tech companies, including Microsoft and Google, are using deep neural networks to design intelligent systems. In deep learning systems, the processes of feature selection and configuration are automated, which means that the networks can choose between the most effective algorithms for hierarchal feature extraction on their own.

We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data. Our devised method consists of a biased tree ensemble that is built according to a probabilistic bias weight distribution. The bias weight distribution is obtained from the assignment of high weights to the drug targets and propagating the assigned weights over a protein-protein interaction network such as STRING. The propagation of weights, defines neighborhoods of influence around the drug targets and as such simulates the spread of perturbations within the cell, following drug administration. Using a synthetic dataset, we showcase how application of biased tree ensembles (BiTE) results in significant accuracy gains at a much lower computational cost compared to the unbiased random forests (RF) algorithm. We then apply NetBiTE to the Genomics of Drug Sensitivity in Cancer (GDSC) dataset and demonstrate that NetBiTE outperforms RF in predicting IC50 drug sensitivity, only for drugs that target membrane receptor pathways (MRPs): RTK, EGFR and IGFR signaling pathways. We propose based on the NetBiTE results, that for drugs that inhibit MRPs, the expression of target genes prior to drug administration is a biomarker for IC50 drug sensitivity following drug administration. We further verify and reinforce this proposition through control studies on, PI3K/MTOR signaling pathway inhibitors, a drug category that does not target MRPs, and through assignment of dummy targets to MRP inhibiting drugs and investigating the variation in NetBiTE accuracy.